Comparing Metacritic and Rotten Tomatoes to see who can better predict CinemaScores and Box Office Gross

(1) movies.R - pulls a list of movies from my iTunes Library

--> MList.csv (109 Data Points)

(2) movies.py - uses BeuatifulSoup to parse rotten tomatoes and prints each movies tomatoscores to a new csv

--> TomatoList.csv (104 Data Points)

(3) meta _ movies.py - uses an Open Movies Database API to pull metacritic scores for each movie and save them to a csv

--> MeatList.csv (92 Data Points)

(4) cinema _ movies.py - uses pyautogui to save each movies cinema score as an image to be added to a csv manually

--> CimenaList1.csv (53 Data Points) - Contains only movies for which the script correctly pulled the score

--> CinemaList2.csv (84 Data Points) - Contains movies in CinemaList1 with the gaps filled in by hand

--> Cinema Folder Contains the image results of the script

(5) boffice _ movies.py - uses the Open Movies Database API to get the boxoffice numbers for each remaining movies

    --> BofficeList.csv (49 Data Points) - uses CinemaList1.csv as base
    
    --> BofficeList2.csv (76 Data Points) - uses CinemaList2.csv as base

(6) regression _ movies.R - Cleans and tries to find relationships between the data. Spoilers: There are none

    --> RList.csv (49 Data Points)
    
    --> Reg Vis folder contains the charts returned from the script

(7) vis _ movies.r - makes pictures with my data